@inproceedings{bc71df1c5a844c0daa26aa80b4ce1f4f,
title = "Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data",
abstract = "Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. The results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. The overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. The computational complexity of SGPR models is O(mn2), and it does not require lengthy model fitting process as SVR and ANN. This makes GP models more practicable for real-world practice in large-scale transportation data analyses.",
keywords = "Freight vehicle, Gaussian Processes, Machine learning, Sparse approximation, Trajectory data, Travel time prediction",
author = "Xia Li and Ruibin Bai",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016 ; Conference date: 12-10-2016 Through 14-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46257-8_16",
language = "English",
isbn = "9783319462561",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "143--153",
editor = "Daoqiang Zhang and Yang Gao and Hujun Yin and Bin Li and Yun Li and Ming Yang and Frank Klawonn and Tall{\'o}n-Ballesteros, {Antonio J.}",
booktitle = "Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings",
address = "Germany",
}